Preliminary population studies of the grassland swallowtail butterfly Euryades corethrus (Lepidoptera, Papilionidae)

Abstract

Euryades corethrus is a Troidini butterfly (Papilionidae, Papilioninae), endemic to grasslands in southern Brazil, Uruguay, Argentina and Paraguay. Formerly abundant, nowadays it is in the Red list of endangered species for those areas. During its larval stage, it feeds on Aristolochia spp, commonly found in southern grasslands. These native grassland areas are diminishing, being converted to crops and pastures, causing habitat loss for Aristolochia and E. corethrus. This study aimed to assess the genetic diversity, population structure and demographic history of E. corethrus. We sampled eight populations from Rio Grande do Sul, Brazil and based on Cytochrome Oxidase subunit I (COI) molecular marker, our results suggest a low genetic variability between populations, presence of gene flow and, consequently, lack of population structure. A single maternally inherited-genetic marker is insufficient for population-level decisions, but barcoding is a useful tool during early stages of population investigation, bringing out genomic diversity patterns within the target species. Those populations likely faced a bottleneck followed by a rapid expansion during the last glaciation and subsequent stabilization in effective population size. Habitat loss is a threat, which might cause isolation, loss of genetic variability and, ultimately, extinction of E. corethrus if no habitat conservation policy is adopted.

Key words
barcoding; COI; conservation; gene flow; phylogeography; population genetics

INTRODUCTION

The main goal of conservation biology is the maintenance of populations and species in the long term, over a wide geographic scope. Among the many hurdles in conservation efforts, we must include the identification of populations and units, the assessment of population characteristics such as size and connectivity, detection of hybridization, evaluation of the persistence and adaptation potentials of populations when faced with environmental change, and ultimately an understanding of the factors affecting this process (Hohenlohe et al. 2021). Population studies can provide lots of information, and although they are insufficient to investigate the factors controlling the abundance and distribution of the plants and animals being studied (Ehrlich & Murphy 1987), they are the first steps in an effort to improve the data on species that otherwise would remain lacking. As demonstrated by Petit-Marty et al. (2021), nucleotide diversity in COI can be a useful tool for preliminary evaluation of the conservation status of a species that lacks previous knowledge

Sadly, conservation studies are biased toward vertebrates and forest areas (Di Marco et al. 2017, Lawler et al. 2006), and as a result, grassland is often overlooked. That apparent lack of diversity is deceiving, since they actually are among the richest ecosystems in the world, in some cases even richer than forest areas (Wilson et al. 2012). Grassland environments are also responsible for various ecological services, among them soil carbon sequestration and hydric resources management (Pillar & Vélez 2010). Two projects, called GLASOD (Global Assessment of Human-induced Soil Degradation) and UNEP (United Nations Environment Programme) project, mapped out the degradation of areas with vegetation cover in the world and found out that some form of antropic degradation is present in 15% of those areas. That number rises to 33% if we consider cultivation areas, according to an FAO (Food and Agriculture Organization) report. While forest coverage and structure can be good indices of conservation in ecoregions clearly dominated by forests, in non-forested ecoregions (e.g. grasslands, deserts), conservation should be based on other variables. Adding to that, Tropical grassy biomes might be mischaracterized as anthropogenic in origin and already degraded due to the lack of tree cover, which causes those areas to be mismanaged. This error can result in avoidance of disturbances (e.g. fires) that are necessary for the dynamics between grasses and trees (Parr et al. 2014). Furthermore, they might suffer “reforestation” with exotic species such as Pinus and Eucalyptus (Canadell & Raupach 2008), causing the replacement of native, ancient and endemic plant species (Bond & Parr 2010, Putz & Redford 2010, Stickler et al. 2009). The three southern states of Brazil (Paraná, Santa Catarina and Rio Grande do Sul) have a peculiar mosaic of biomes, where the Pampa in the southernmost areas turns into Atlantic forest as we go north and finally a small area of Cerrado at the borders of Paraná and São Paulo. The forest areas in the states of Santa Catarina and Paraná and in the northern half of Rio Grande do Sul have patches of grassland while the South Brazilian Plateau is mostly grassland (Pampa). The Pampa biome has been reduced to about 43% of its original size, mainly due to the conversion to agriculture and silviculture (Vélez-Martin et al. 2015) and yet, those areas are neglected and not properly protected (Overbeck et al. 2007). Adding to that, knowledge on insect diversity in the Pampa biome is severely lacking. On the other hand, if we consider that Mendonça et al. (2015) states that studies have found up to 17 different orders of arthropods in the grasslands of the Pampa biome, it is safe to speculate that those areas have a high diversity of insects. An evidence of that is the data available on iNaturalist, where a group called “Insects of Rio Grande do Sul” has gathered occurrences for 2117 different species (iNaturalist).

Also, Rio Grande do Sul is among the richest when it comes to butterflies, with 832 known species and subspecies (Giovenardi et al. 2013), and among those species, there is Euryades corethrus, a Neotropical swallowtail butterfly from the tribe Troidini (Papilionidae). This species is endemic to grasslands of southern regions of Brazil and neighbouring countries (Uruguay, Paraguay and Argentina) (Tyler et al. 1994) and are more easily found from September to April.

Euryades corethrus feeds on plants of the genus Aristolochia while on their larval stage and on plants such as Senecio and Eupatorium during the adult stage. Those plants are common in southern native grasslands (Costa 2016). According to the available literature (Biezanko et al. 1974, Klitzke & Brown 2000, Beccaloni et al. 2008, Volkmann & Núñez-Bustos 2010), E. corethrus uses five Aristolochia species as host plants during its larval stage. In our personal observation, whenever we encountered the larval butterfly in the field, it was feeding on A. sessilifolia. This could indicate that E. corethrus has a strong association to this particular species, but without further investigation we can only speculate. Many butterflies (with E. corethrus being one example), are highly sensitive to environmental changes. They are habitat specialists with moderate long range dispersal abilities, and as a consequence, climate-driven range shifts can directly impact their phylogeographical structure. An example of that effect for a similar swallowtail that occurs in a temperate mountainous habitat can be seen in Todisco et al. (2012) Apart from climate change effects, impacts caused by human activities (like the progressive shrinking of native grassland areas where the host plant can be found) could be compounded by climate-driven range shifts. According to Tyler et al. (1994), this used to be an abundant butterfly species in its distribution area; however, nowadays E. corethrus is listed in different categories in the Red list of endangered species for the Brazilian states of Rio Grande do Sul (vulnerable - VU) (Rio Grande do Sul 2014), Santa Catarina (endangered - EN) (Santa Catarina 2011) and Paraná (endangered - EN), (Instituto Ambiental do Paraná 2007). On a global level, the IUCN placed E. corethrus in the Near Threatened (NT) category (Grice et al. 2019), according to their latest assessment for the Red list of Threatened Species.

The use of charismatic species to attract public support for a particular area or biome (Andelman & Fagan 2000) is a strategy that could be applied using E. corethrus. It can perform this role by acting as a surrogate, becoming an umbrella or an indicator species for the grassland. The approach of “indicator species” is based on the premise that the chosen species (in that case, E. corethrus) is reflecting the other species in the community, as well as reflecting the chemical and/or physical changes to that environment (Landres et al. 1988)

Despite being a very good candidate for the role of flagship species, E. corethrus hasn’t been extensively studied: records in the literature are mainly inventories or records of new occurrences (Dolibaina et al. 2011), with a few studies dealing with the biology of the species (Caporale et al. 2017), only recently focusing on its ecology (Costa 2016). Euryades corethrus has been tangentially investigated during studies dealing with the tribe Troidini, such as their origin and evolution (Braby et al. 2005), phylogenetic relationships within the tribe (Silva-Brandão et al. 2015) and biogeography and diversification patterns (Condamine et al. 2012). They were included in the phylogenetic relationships and divergence times of Papilionidae (Condamine et al. 2012, Allio et al. 2020) but an investigation focused on this particular species’ relationship with the other members of Papilionidae still hasn’t been done. Moreover, there is none specifically analyzing the genetic diversity and population structure of

E. corethrus. Holsinger & Weir (2009) defines a structured population as one that has different variances among populations and within populations. There is a substructure instead of the same variance throughout the whole population. Nordborg & Innan (2002) see that substructure as a perturbation of the standard neutral model. A structured population has less gene flow, which can eventually lead to isolation and speciation, but also raises the risk of extinction. On the other hand, gene flow is a “creative force”, preventing speciation and natural selection and therefore inhibiting genetic evolution (Slatkin 1987). Knowing the extinction risks and mapping priority areas needed to preserve not only individuals but also the species’ genetic diversity are invaluable tools for defining conservation strategies.

Considering E. corethrus is an endangered species in need of urgent conservation actions and it is unknown how habitat loss will affect the remaining populations, or even if and how those populations are structured, this work was aimed at obtaining preliminary data regarding the genetic diversity, population structure, gene flow and demographic history of E. corethrus from southern Brazil.

MATERIALS AND METHODS Specimen collection

The expected area of occurrence included the Brazilian states of Rio Grande do Sul, Santa Catarina and Paraná and also areas of Argentina and Uruguay that border those states. All specimens were captured in their adult stage, in the field, between March of 2016 and January of 2017. The exceptions are the specimens from Eldorado do Sul, Rio Grande do Sul (CE), which were provided by a collaborator after being collected in the field as eggs and reared in the lab until they reached the adult stage. We cannot ensure that siblings were not compared, but since they were collected on different days and that this butterfly has the habit of placing a single egg (occasionally up to five) (Caporale et al. 2017, Mega et al. 2020) on each plant. We cannot be certain if individuals within an area were part of the same brood. A total of 80 individuals from 8 different localities were used (Table I). The collecting sites were chosen based on previous knowledge about the expected distribution of the butterflies and known remnants of preserved grassland (G.W.G Atencio et al. unpublished data). In order to test if the butterflies form just a single population or several smaller populations within the considered area, we chose to include in the DNA extraction only specimens from sites with at least 10 captured specimens and localities as widespread as possible within the known area of occurrence. Captured specimens were killed by pinching, kept in individual, identified paper envelopes, frozen as soon as possible and kept at -20°C freezer until DNA extraction. The butterflies were collected under SISBIO permit number 52634-1.

Table I
Localities, collection sites, coordinates, sample codes and accession numbers (GenBank) of Euryades corethrus from Southern Brazil.

Locality	Collection site	Coordinates	Sample	Accession number
Eldorado do Sul	Estação Experimental Agronômica de Eldorado do Sul	-30.108977, -51.675674	CE02	MN510336
			CE04	MN510337
			CE05	MN510338
			CE06	MN510339
			CE08	MN510340
			CE09	MN510341
			CE11	MN510342
			CE12	MN510343
			CE16	MN510344
			CE22	MN510345
Barra do Quaraí	Parque Nacional do Espinilho	-30.273140, -57.476846	E01	MN510346
			E02	MN510347
			E03	MN510348
			E04	MN510349
			E06	MN510350
			E07	MN510351
			E08	MN510352
			E09	MN510353
			E10	MN510354
			E11	MN510355
Júlio de Castilhos	Fazenda Bom Retiro	-29.13961, -53.65226	FBR01	MN510356
			FBR02	MN510357
			FBR03	MN510358
			FBR04	MN510359
			FBR08	MN510360
			FBR10	MN510361
			FBR12	MN510362
			FBR13	MN510363
			FBR15	MN510364
			FBR17	MN510365
	Fazenda Mauro Silva	-29.11981, -53.45107	FMS01	MN510366
			FMS02	MN510367
			FMS03	MN510368
			FMS04	MN510369
			FMS08	MN510370
			FMS09	MN510371
			FMS10	MN510372
			FMS11	MN510373
			FMS12	MN510374
			FMS13	MN510375
Santo Antônio das Missões	Fazenda Iviretã	-28.58186, -55.64873	FZI01	MN510376
			FZI02	MN510377
			FZI03	MN510378
			FZI04	MN510379
			FZI05	MN510380
			FZI06	MN510381
			FZI07	MN510382
			FZI08	MN510383
			FZI09	MN510384
			FZI10	MN510385
Santana do Livramento	Lago do Batuva	-30.86711, -55.52462	S01	MN510386
			S02	MN510387
			S03	MN510388
			S04	MN510389
			S05	MN510390
			S06	MN510391
			S07	MN510392
			S08	MN510393
			S11	MN510394
			S12	MN510395
Santo Antônio das Missões	Fazenda Querência	-28.5705, -55.61827	SA02	MN510396
			SA03	MN510397
			SA05	MN510398
			SA07	MN510399
			SA09	MN510400
			SA11	MN510401
			SA12	MN510402
			SA14	MN510403
			SA16	MN510404
			SA17	MN510405
Uruguaiana	Fazenda São Felipe	-29.69835, -56.90919	U01	MN510406
			U12	MN510407
			U13	MN510408
			U14	MN510409
			U15	MN510410
			U16	MN510411
			U17	MN510412
			U18	MN510413
			U19	MN510414
			U20	MN510415

DNA extraction and amplification

Total genomic DNA was extracted from thorax following Mega & Ravers (2011). DNA quality and concentration were measured using a spectrophotometer (Nanodrop ND˗1000, Thermo Fisher). The COI gene fragment was amplified by PCR using the universal primers LCO˗1490 (forward) and HCO˗2198 (reverse) from Hebert et al. (2003). PCR was conducted in 10 µL volume reactions, as follows: 6.75 µL ultrapure water, 1.0 µL 10x PCR buffer, 0.5 µL MgCl₂, 0.25 µL dNTP mix (10 mM), 0.2 µL of each primer (20 mM), 0.1 µL Platinum Taq DNA polymerase (5 U/µL), 1 µL DNA (approximately 50 ng). Thermal cycling conditions followed Doorenweerd et al. (2015), with modifications: 94°C for 3 minutes; 30 cycles of 94° for 30s, 50°C for 30s and 57°C for 40s; final extension at 72°C for 5 minutes. PCR products were purified with ExoSAP˗IT^TM (Applied Biosystems). Sequencing reactions were performed both forward and reverse at Macrogen (Seoul, South Korea). Electropherograms were assembled, checked and sequences were edited and aligned using both strands for confirmation with Geneious Prime 2019.2.1 (https://www.geneious.com). All sequences were aligned with MAFFT (Rozewicki et al. 2019), implemented in Geneious Prime 2019.2.1. The generated sequences were compared with available sequences using BLASTN (https://blast.ncbi.nlm.nih.gov), with standard parameters and submitted to GenBank (Table I). The sequenced specimens were registered in SISGEN under the number A7278D9.

Genetic diversity and population structure

Maximum parsimony haplotype networks were reconstructed to illustrate relationships among haplotypes of E. corethrus using the Median joining algorithm in PopArt (Bandelt et al. 1999, Leigh & Bryant 2015). The pairwise genetic difference was estimated for all populations by calculating Wright’s F-statistics (Fst) in Arlequin 3.5.2.2 (Excoffier & Lischer 2010). The variation among and within populations was estimated by Analysis of Molecular Variance (AMOVA) in Arlequin 3.5.2.2 using 1,000 permutations.

The population structure was evaluated by spatial analysis of molecular variance (SAMOVA) implemented in SAMOVA 2.0 (Excoffier et al. 1992, Dupanloup et al. 2002, Excoffier & Lischer 2010), without a priori information about population structure and also by Bayesian Analysis of Population Structure (BAPS) 6.0 with the ‘spatial clustering of groups’ models followed by admixture analysis (Corander & Marttinen 2006, Corander et al. 2008a, b, Cheng et al. 2013). BAPS uses both approaches to determine the most appropriate clustering (default model) and best groups in a fixed number of clusters (fixed-K mode).

Tests of demographic equilibrium and population expansion

Haplotype and nucleotide diversity were calculated using DnaSP 6.0 (Rozas et al. 2017). Demographic equilibrium was tested in each operational population unit by calculating Fu’s Fs (Fu 1997) and Tajima’s D (Tajima 1989) statistics. P-values for the two statistics were obtained as the proportion of simulated values smaller than or equal to the observed values (ɑ = 0.02 for Fu’s Fs, and ɑ = 0.05 for Tajima’s D). Expected mismatch distributions and parameters of sudden expansion s = 2lT were calculated using Arlequin 3.5.2.2 by a generalized least-squares approach (Schneider and Excoffier 1999), under models of pure demographic expansion and spatial expansion (Ray et al. 2003, Excoffier 2004). The probability of the data under the given model was assessed by the goodness-of-fit test implemented in Arlequin. Parameter confidence limits were calculated in Arlequin through a parametric bootstrap (10,000 simulated random samples).

Reconstruction of demographic histories

The demographic history of the population was estimated by running the MCMC analysis under the Bayesian skyline tree prior in BEAST 2.6.2 (Bouckaert et al. 2019). The HKY (Hasegawa, Kishino and Yano) model for nucleotide substitution (Hasegawa et al. 1985) was estimated in JModelTest. We assumed a relaxed lognormal molecular clock with a mean rate of 0.0075 (corresponding to the divergence rate of 1.5% per million years). This rate is commonly used in Lepidoptera studies (DeChaine & Martin 2005, Canfield et al. 2008, Kawakita & Kato 2009, Oliver et al. 2012, Pfeiler et al. 2012, Wang et al. 2014). Tracer 1.7.1 was used to reconstruct the Bayesian skyline plot (BSP) (Drummond et al. 2005).

RESULTS

Genetic diversity

The dataset of COI alignment contains 657 bp, of which 16 were variable (13 synonymous and 3 non-synonymous substitutions) and 6 parsimony informative. These polymorphic sites defined 17 haplotypes (Figure 1). The haplotype diversity (Hd) ranged from 0.378 in CE to 0.667 in FMS, FZI, SA and U. The nucleotide diversity (π) ranged from 0.00061 in CE to, 0.00122 in FMS, FZI, SA and U. The summary of genetic diversity indices and neutrality tests are presented in Table II. From the 17 haplotypes recovered, 10 haplotypes were unique to a single locality and one haplotype was found in all localities. The remaining haplotypes range from 2 to 4 localities (Figure 2). The most common haplotype was present in all population samples and was the most abundant haplotype across all populations, identified in 70% of all the sampled individuals across the entire study region. This main haplotype was the core of a star-like cluster topography, central to all other haplotypes, most of them represented by a single individual and divergent by a single mutation (Figure 1).

Figure 1
Haplotype network obtained from COI haplotypes of Euryades corethrus sampled in RS, Southern Brazil. Each circle represents a different haplotype, with the size of each circle proportional to the number of individuals displaying that particular haplotype. The haplotypes found in more than one individual are identified and represented by pie charts; Different colours represent the locations where that haplotype was found. Dashes indicate the number of mutational steps between each haplotype. (CE = Estação Experimental Agronômica de Eldorado do Sul - Eldorado do Sul, E = Parque Nacional do Espinilho - Barra do Quaraí, FBR =Fazenda Bom Retiro - Júlio de Castilhos, FMS = Fazenda Mauro Silva - Júlio de Castilhos, FZI = Fazenda Iviretã - Santo Antônio das Missões, S = Lago do Batuva - Santana do Livramento, SA = Fazenda Querência - Santo Antônio das Missões, U = Fazenda São Felipe - Uruguaiana).

Figure 2
Geographical distribution of the haplotypes of Euryades corethrus from Southern Brazil. The size of the pie charts is proportional to the number of individuals sampled from each population, and each colour in the pie charts represents a different haplotype; The same haplotype found in different populations is represented by the same colour in the corresponding pie charts. (CE = Estação Experimental Agronômica de Eldorado do Sul - Eldorado do Sul, E = Parque Nacional do Espinilho - Barra do Quaraí, FBR =Fazenda Bom Retiro - Júlio de Castilhos, FMS = Fazenda Mauro Silva - Júlio de Castilhos, FZI = Fazenda Iviretã - Santo Antônio das Missões, S = Lago do Batuva - Santana do Livramento, SA = Fazenda Querência - Santo Antônio das Missões, U = Fazenda São Felipe - Uruguaiana).

Table II
Summary of genetic diversity indices and neutrality tests for Euryades corethrus.

Locale	N	S	h	Haplotypes	Hd±SD	π±SD	Tajima’s D	Fu’s Fs
CE	10	2	3	H1, H2, H3	0.378±0.181	0.00061±0.00031	-1.40085 p=0.096	-1.164 p=0.039
E	10	3	4	H1, H3, H4, H5	0.533±0.180	0.00091±0.00036	-1.56222 p=0.03*	-1.964 p=0.008*
FBR	10	2	3	H1,H6, H7	0.417±0.191	0.00068±0.00034	-1.40085 p=0.085	-1.164 p=0.032
FMS	10	4	5	H1, H3,H8,H9,H10	0.667±0.163	0.00122±0.00039	-1.66706 p=0.037*	-2.847 p=0.003*
FZI	10	4	5	H1, H3, H11, H12, H13	0.667±0.163	0.00122±0.00039	-1.66706 p=0.032*	-2.847 p=0.001*
S	10	3	4	H1, H2, H4, H14	0.533±0.180	0.00091±0.00036	-1.56222 p=0.055	-1.964 p=0.013*
SA	10	4	5	H1, H4, H15, H16, H17	0.667±0.163	0.00122±0.00039	-1.66706 p=0.003*	--2.8472 p=0.003*
U	10	4	5	H1, H2, H6, H11, H13	0.667±0.163	0.00122±0.00039	-1.66706 p=0.034*	-2.847 p=0.002*
Total	80	16	17	H1-H17	0.542± 0.068	0.00097± 0.00015	-2.31193 p=0.000*	--20.832 p=0.000*

• (N) Number of individuals sampled; (S) number of polymorphic sites; (h) number of haplotypes; (Hd) haplotype diversity; (π) nucleotidic diversity with standard deviation (SD); (D) and (F) statistics for neutrality test. Significance level for D is 0.05 and for F is 0.02.

Fu’s Fs and Tajima’s D statistics were calculated for all operational population units defined a priori and for a unique population approach. Values of Fs and D were negative for all populations, and significant for most of them (Table II). In stable populations, over time, it is expected for those values to be close to zero. Since our values were negative and significant for most of them, the null hypothesis of constant population size should be rejected. Just as is the case with Tajima’s D, the value calculated for Fu’s Fs is an indication of population expansion.

Mismatch analysis corroborates the sudden expansion model (Table III). The theta value (0.05 ma) indicates the true time since population expansion. The observed mismatch distributions corresponded to the expected frequency distributions of pairwise nucleotide site differences in an exponentially growing population (Figure 3). Also, the mismatch distribution was unimodal indicating demographic expansion.

Figure 3
Frequency distributions of the observed pairwise nucleotide site differences, or mismatch distributions for the Euryades corethrus population from Southern Brazil, compared to expected frequency distributions for a population under demographic expansion (3a) and for constant demographic size (3b).

Table III
Mismatch analysis of the Euryades corethus population from Southern Brazil under sudden expansion model and spatial expansion model.

	Sudden expansion model	Spatial expansion model1
Tau	0.7	0.6
Tau 5%	0.49	0.37
Tau 95%	1.54	2.12
Theta (Ma)	0.05	0.20
Theta 5%	0.00	0.00072
Theta 95%	0.35	0.47
Corrected p-value	0.04*	0.29

• ¹ Parameters of the spatial expansion assuming constant demographic size.
  Theta= true time since population expansion.
  *Significant p-value.

Population structure

The population structure was evaluated by spatial analysis of molecular variance (SAMOVA) without a priori information about population structure and also by Bayesian Analysis of Population Structure (BAPS). Both analyses presented similar results, indicating a lack of population structure and therefore suggesting the populations defined a priori based on the geographic location are a unique panmictic population. The analysis of molecular variance (AMOVA) calculated the source of variation among and within populations (Sum of squares was 1.900 and 23.400, respectively); the variation within populations corresponded to 100% of total variation. Also, the average Fst value was -0.01676 (p value = 1) indicating no genetic differentiation between populations. Fst values are supposed to range from 0 to 1, but small samples with low differentiation levels can produce negative values close to zero. Pairwise Fst values for all populations are represented in Figure 4.

Figure 4
Matrix of pairwise Fst (Wright’s F-statistics) for Euryades corethrus populations from Southern Brazil.(CE = Estação Experimental Agronômica de Eldorado do Sul - Eldorado do Sul, E = Parque Nacional do Espinilho - Barra do Quaraí, FBR =Fazenda Bom Retiro - Júlio de Castilhos, FMS = Fazenda Mauro Silva - Júlio de Castilhos, FZI = Fazenda Iviretã - Santo Antônio das Missões, S = Lago do Batuva - Santana do Livramento, SA = Fazenda Querência - Santo Antônio das Missões, U = Fazenda São Felipe - Uruguaiana).

Demographic history

The effective sample size (ESS) for the Bayesian Skyline Plot was larger than 200, suggesting that the MCMC (Markov chain Monte Carlo) mixed properly and that the number of generations was sufficient to infer size changes.

The Bayesian skyline plot indicated an overall pattern of population stability throughout the Pleistocene. A fluctuation near the present (around 100,000 ya), suggests a population bottleneck followed by rapid population expansion (~50,000 ya). (Figure 5).

Figure 5
Demographic history of Euryades corethrus population from Southern Brazil reconstructed using Bayesian skyline plot based on COI gene with divergence rate of 1.5% per million years. X-axis is the timescale before present, and Y-axis is the estimated effective population size. Solid curve indicates median effective population size; the shaded area indicates 95% highest posterior density (HPD) intervals. Time is represented in million years (Ma).

DISCUSSION

Although typically not sufficient when unraveling population-level questions, mitochondrial (mt) COI (Cytochrome Oxidase I) barcoding is a useful tool during the early stages of population structure investigation, highlighting patterns of genomic diversity within the target species (Hajibabaei et al. 2007). We believe our findings are useful as a starting point, but should be approached carefully, since we are dealing with a single genetic marker with a rather idiosyncratic evolutionary history. It is, after all, maternally inherited and much smaller than the nuclear genome, but it is still useful information for a species that severely lacks genetic data, as is the case for E. corethrus.

The COI sequences of E. corethrus revealed very low nucleotide diversity (0.00097± 0.00015) and a relatively high haplotype diversity (0.542± 0.068). Despite the haplotype diversity, most occur at a very low frequency and are differentiated from a dominant (probably ancestral) haplotype by a single mutational step. Tajima’s D and Fu’s Fs values were negative for all populations and significant for most of them, indicating an excess of rare nucleotide site variants compared to the expectation under a neutral model of evolution. These results lead us to reject the neutrality hypothesis.

A negative Tajima’s D, paired with the haplotype network in a “star-like” pattern (Figure 1) (Slatkin & Hudson 1991, Avise 2000) indicates population size expansion after a bottleneck or a selective sweep and/or purifying selection (Tajima 1989). Further evidence is found in the aforementioned negative value of Fs (evidence for an excess number of alleles), which is expected to follow a recent population expansion or from genetic hitchhiking (Fu 1997).

Schmitt & Seitz (2002) investigated the genetic structure of Polyommatus coridon (Lepidoptera: Lycaenidae) and also verified very low Fst values, just like our results for E. corethrus. They also didn’t find hierarchical structure either between or within the study areas, as well as no correlation between genetic and geographical distances. This lack of congruence between geographical and genetic patterns might be explained by the high dispersal power of some species, corroborated by Hastings & Harrison (1994). Our results suggest that populations from Southern Brazil probably represent one metapopulation sufficiently interconnected for the maintenance of the genetic pool. The sufficient gene flow between populations reduces or restrain the process of geographic differentiation (de Jong et al. 2011). This phenomenon is common in flying insects, especially in migratory and/or good dispersers, such as Monarch butterflies (Brower & Boyce 1991), bumble bees Bombus terrestris (Estoup et al. 1996), and dragonflies Anax junius (Freeland et al. 2003) (review in de Jong et al. 2011). According to Lv et al. (2019), species with high dispersal ability and with a high number of habitat patches have a better chance of maintaining gene flow and panmixia. Papilionidae are known as strong fliers, so this might be a factor.

The BPS pattern suggests a long period of population stability, with a decrease in population size around 100,000 ya. This population decrease was followed by a rapid population growth around 50,000 ya. A similar pattern, where there was a decrease in population size followed by rapid population growth and range expansion during the Late Pleistocene can be found for other Lepidoptera, like Gnopharmia colchidaria (Geometridae), G. kasrunensis (Geometridae) and Polyommatus coridon (Lycaenidae) (Rajaei Sh et al. 2013, Schmitt & Seitz 2001).

The recent demographic expansion is in accordance with broadly observed patterns of population expansion in different taxa, following the last glacial period, which ended around 12,500 years ago (de Jong et al. 2011). Examples of this pattern can be found in newts (Taricha granulosa, Urodela: Salamandridae) (Kuchta & Tan 2004), Oak (Quercus spp., Fagales: Fagaceae) (Brewer et al. 2002), wild boars (Sus scrofa, Suidae) (Alexandri et al. 2012), flies (Drosophila maculifrons, Diptera: Drosophilidae) (De Ré et al. 2014) and butterflies (Zerynthia polyxena, Lepidoptera: Papilionidae) (Dapporto 2009). The glacial maximum period was characterized by cooler and drier conditions, which caused the contraction of forest areas into small refugia, causing a decrease of the available habitat for many species (Prentice & Jolly 2000).

Contrasting with this, the grassland ecosystems and open vegetation landscapes had expanded through South America, creating new suitable areas during the glaciation (Behling 2002). Species from open areas, belonging to different groups, such as mammals (Mapelli et al. 2012, Mora et al. 2013, Peçanha et al. 2017), reptiles (Felappi et al. 2015), insects (Cristiano et al. 2016) and plants (Fregonezi et al. 2013) respond to Pleistocene climate changes in a different way than species that inhabit forests. According to Turchetto-Zolet et al. (2012), 68% of studies in populations associated with open vegetation showed population expansion during glacial cycles and/or fragmentation during interglacial periods, so our results seem to be in line with these findings.

Apart from the populations of Estação Agronômica de Eldorado do Sul (CE), (which is an area under permanent management), the remaining populations are distributed in small grassland patches amidst a matrix of soy monoculture and livestock areas. Historically, E. corethrus was abundant and the extension of occurrence spanned through the three southern states of Brazil (Tyler et al. 1994). Recent records, however, are rare (G.W.G Atencio et al. unpublished data) and the largest populations are probably the ones reported in this study. Although our results are consistent with population expansion, this reflects the expansion of open areas during the last glaciation. The habitat degradation is a relatively recent event and might cause population isolation, genetic depletion and risk of extinction for E. corethrus if no habitat conservation policy is adopted in the near future. The habitat fragmentation can erode neutral and adaptive genetic diversity of populations caused by decreases in the effective population size and inter-populational gene flow (Johansson et al. 2007, Dixo et al. 2009, Wang et al. 2011, Liu et al. 2013, review in Lv et al. 2019). Thus, habitat fragmentation threatens not only local, but regional, and ultimately global biodiversity (Tilman et al. 1994, Dobson 1997).

Euryades corethrus is inserted in an anthropogenic landscape consisting of highly fragmented agricultural landscapes, with embedded habitat remnants. In such a scenario, the butterflies need not only large and reachable habitats, but also smallers habitats in between that function as stepping stones in order to achieve long-term persistence at the landscape level (Heinrichs et al. 2015). This seems to agree with the strategy proposed by Seraphim et al. (2016), who advise that instead of creating biologic reserves, it is more effective to increase the conservation status of small remnants of suitable habitat, protect the patches that are already inserted in the metropolitan matrix and promote the connectivity or the populations through tree lined avenues and cultivation of native resting flora in urban parks. This is desirable since a metapopulation structure needs a balance between subpopulation diversity and migration in order to maintain the genetic diversity (Whitlock 2004).

Another viable strategy seems to be spatial risk spreading, a measure that compensates for the lag between the measures being taken to restore the habitats and the desired effect in the quantity and quality of habitats (Maes et al. 2004). Among the risk spreading strategies, we can mention translocations to suitable, unoccupied sites that otherwise would have a low probability of short-term spontaneous colonization or even re-introductions into sites previously occupied by the species (Oates 1992).

Regardless of the adopted strategy, it is possible that Rio Grande do Sul has become the last refuge of the species in Brazil. A recent work by G.W.G. Atencio et al. (unpublished data) surveyed the areas where the butterfly was expected to be found but very few places actually had either the butterfly or suitable grassland. Argentina and Uruguay have only very few scattered records in recent years, but the conservation status of native grasslands in these countries seems to be as dire as the one in Brazil, according to G.W.G Atencio et al. (unpublished data) and the relationship between these populations and the ones in Brazil remains to be investigated. We hope that the similarities between the areas in Brazil and the areas in the neighbouring countries of Uruguay and Argentina allow our data to be used for grassland research not only on where data was collected, but in similar areas in South America as well, since conservation efforts should aim to protect not just the Pampa, but the entire grassland network .

ACKNOWLEDGMENTS

This article is a chapter of the PhD thesis of the first author. We would like to thank Dr. Vera Lúcia da Silva Valente Gayeski for allowing us to use the Drosophila lab and all its equipment to perform the wetlab experiments, as well as supporting us in all stages of this work. Grant sponsor: Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); Brazil. Grant number: 163268/2015-0.

REFERENCES

• ALEXANDRI P, TRIANTAFYLLIDIS A, PAPAKOSTAS S, CHATZINIKOS E, PLATIS P, PAPAGEORGIOU N, LARSON G, ABATZOPOULOS TJ & TRIANTAPHYLLIDIS C. 2012. The Balkans and the colonization of Europe: The post-glacial range expansion of the wild boar, Sus scrofa: Post-glacial range expansion of wild boar. J Biogeogr 39(4): 713-723.
• ALLIO R, SCORNAVACCA C, NABHOLZ B, CLAMENS A-L, SPERLING FA & CONDAMINE FL. 2020. Whole Genome Shotgun Phylogenomics Resolves the Pattern and Timing of Swallowtail Butterfly Evolution. Syst Biol 69(1): 38-60.
• ANDELMAN SJ & FAGAN WF. 2000. Umbrellas and flagships: Efficient conservation surrogates or expensive mistakes? Proceedings of the National Academy of Sciences 97(11): 5954-5959. https://doi.org/10.1073/pnas.100126797.
  » https://doi.org/10.1073/pnas.100126797
• AVISE JC. 2000. Phylogeography: the history and formation of species. Cambridge: Harvard university press, 447 p.
• BANDELT HJ, FORSTER P & RÖHL A. 1999. Median-joining networks for inferring intraspecific phylogenies. Mol Biol Evol 16(1): 37-48.
• BECCALONI GW, HALL SK, VILORIA AL & ROBINSON GS. 2008. Catalogue of the hostplants of the Neotropical Butterflies / Catálogo de las plantas huéspedes de las mariposas Neotropicales, 8. Zaragoza: Sociedad Entomológica Aragonesa, 536 p.
• BEHLING H. 2002. South and southeast Brazilian grasslands during Late Quaternary times: a synthesis. Palaeogeogr Palaeoclimatol Palaeoecol 177(1): 19-27.
• BIEZANKO CM, RUFFINELLI A & LINK D. 1974. Plantas y otras sustancias alimenticias de las orugas de los lepidopteros uruguayos. Rev Cent Cienc Rurais 4(2): 107-148.
• BOND WJ & PARR CL. 2010. Beyond the forest edge: Ecology, diversity and conservation of the grassy biomes. Biol Conserv 143(10): 2395-2404.
• BOUCKAERT R ET AL. 2019. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLOS Comput Biol 15(4): e1006650.
• BRABY MF, TRUEMAN JWH & EASTWOOD R. 2005. When and where did troidine butterflies (Lepidoptera: Papilionidae) evolve? Phylogenetic and biogeographic evidence suggests an origin in remnant Gondwana in the Late Cretaceous. Invertebr Syst 19: 113-143.
• BREWER S, CHEDDADI R, DE BEAULIEU JL & REILLE M. 2002. The spread of deciduous Quercus throughout Europe since the last glacial period. For Ecol Manag 156(1-3): 27-48. https://doi.org/10.1016/S0378-1127(01)00646-6.
  » https://doi.org/10.1016/S0378-1127(01)00646-6
• BROWER AVZ & BOYCE TM. 1991. Mitochondrial DNA Variation in Monarch Butterflies. Evolution 45(5): 1281-1286.
• CANADELL JG & RAUPACH MR. 2008. Managing Forests for Climate Change Mitigation. Science 320(5882): 1456-1457.
• CANFIELD MR, GREENE E, MOREAU CS, CHEN N & PIERCE NE. 2008. Exploring phenotypic plasticity and biogeography in emerald moths: A phylogeny of the genus Nemoria (Lepidoptera: Geometridae). Mol Phylogenet Evol 49(2): 477-487.
• CAPORALE A, ROMANOWSKI HP & MEGA NO. 2017. Winter is coming: Diapause in the subtropical swallowtail butterfly Euryades corethrus (Lepidoptera, Papilionidae) is triggered by the shortening of day length and reinforced by low temperatures. J Exp Zool Part A: Ecol Integr Physiol 327(4): 182-188. https://doi.org/10.1002/jez.2091.
  » https://doi.org/10.1002/jez.2091
• CHENG L, CONNOR TR, SIRÉN J, AANENSEN DM & CORANDER J. 2013. Hierarchical and spatially explicit clustering of DNA sequences with BAPS software. Mol Biol Evol 30(5): 1224-1228.
• CONDAMINE FL, SILVA-BRANDÃO KL, KERGOAT GJ & SPERLING FA. 2012. Biogeographic and diversification patterns of Neotropical Troidini butterflies (Papilionidae) support a museum model of diversity dynamics for Amazonia. BMC Evol Biol 12(1): 82.
• CORANDER J & MARTTINEN P. 2006. Bayesian identification of admixture events using multilocus molecular markers. Mol Ecol 15(10): 2833-2843.
• CORANDER J, MARTTINEN P, SIRÉN J & TANG J. 2008a. Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations. BMC Bioinformatics 9(1): 539.
• CORANDER J, SIRÉN J & ARJAS E. 2008b. Bayesian spatial modeling of genetic population structure. Computation Stat 23(1): 111-129.
• COSTA MC. 2016. História natural e biologia populacional em Euryades corethrus (Lepidoptera: Papilionidae: Troidini), uma espécie brasileira ameaçada de extinção. Universidade Federal do Rio Grande do Sul.
• CRISTIANO MP, CARDOSO DC, FERNANDES-SALOMÃO TM & HEINZE J. 2016. Integrating Paleodistribution Models and Phylogeography in the Grass-Cutting Ant Acromyrmex striatus (Hymenoptera: Formicidae) in Southern Lowlands of South America. PLoS ONE 11(1): e0146734.
• DAPPORTO L. 2009. Speciation in Mediterranean refugia and post-glacial expansion of Zerynthia polyxena (Lepidoptera, Papilionidae). J Zool Syst Evol Res 48: 229-237. https://doi.org/10.1111/j.1439-0469.2009.00550.x.
  » https://doi.org/10.1111/j.1439-0469.2009.00550.x
• DECHAINE EG & MARTIN AP. 2005. Historical biogeography of two alpine butterflies in the Rocky Mountains: broad-scale concordance and local-scale discordance. J Biogeogr 32(11): 1943-1956.
• DE JONG MA, WAHLBERG N, VAN EIJK M, BRAKEFIELD PM & ZWAAN BJ. 2011. Mitochondrial DNA Signature for Range-Wide Populations of Bicyclus anynana Suggests a Rapid Expansion from Recent Refugia D. Bhattacharya, ed. PLoS ONE 6(6): e21385.
• DI MARCO M ET AL. 2017. Changing trends and persisting biases in three decades of conservation science. Glob Ecol Conserv 32-42.
• DIXO M, METZGER JP, MORGANTE JS & ZAMUDIO KR. 2009. Habitat fragmentation reduces genetic diversity and connectivity among toad populations in the Brazilian Atlantic Coastal Forest. Biol Conserv 142(8): 1560-1569.
• DOBSON AP, BRADSHAW AD & BAKER AJM. 1997. Hopes for the Future: Restoration Ecology and Conservation Biology. Science 277(5325): 515-522.
• DOLIBAINA DR, MIELK OHH & CASAGRANDE MM. 2011. Borboletas (Papilionoidea e Hesperioidea) de Guarapuava e arredores, Paraná, Brasil: um inventário com base em 63 anos de registros. Biota Neotrop 11(1): 341-354.
• DOORENWEERD C, VAN NIEUKERKEN EJ & MENKEN SBJ. 2015. A global phylogeny of leafmining Ectoedemia moths (Lepidoptera: Nepticulidae): Exploring host plant family shifts and allopatry as drivers of speciation. PLoS ONE 10(3): 1-20.
• DRUMMOND AJ, RAMBAUT A, SHAPIRO B & PYBUS OG. 2005. Bayesian Coalescent Inference of Past Population Dynamics from Molecular Sequences. Mol Biol Evol 22(5): 1185-1192.
• DUPANLOUP I, SCHNEIDER S & EXCOFFIER L. 2002. A simulated annealing approach to define the genetic structure of populations. Mol Ecol 11(12): 2571-2581.
• EHRLICH PR & MURPHY DD. 1987. Conservation Lessons from Long-Term Studies of Checkerspot Butterflies. Conserv Biol 1(2): 122-131.
• ESTOUP A, SOLIGNAC M, CORNUET JM, GOUDET J & SCHOLL A. 1996. Genetic differentiation of continental and island populations of Bombus terrestris (Hymenoptera: Apidae) in Europe. Mol Ecol 5(1): 19-31.
• EXCOFFIER L. 2004. Patterns of DNA sequence diversity and genetic structure after a range expansion: lessons from the infinite-island model. Mol Ecol 13(4): 853-864.
• EXCOFFIER L & LISCHER HEL. 2010. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10(3): 564-567.
• EXCOFFIER L, SMOUSE PE & QUATTRO JM. 1992. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131(2): 479-491.
• FELAPPI JF, VIEIRA RC, FAGUNDES NJR & VERRASTRO LV. 2015. So Far Away, Yet So Close: Strong Genetic Structure in Homonota uruguayensis (Squamata, Phyllodactylidae), a Species with Restricted Geographic Distribution in the Brazilian and Uruguayan Pampas. PLoS ONE 10(2): e0118162.
• FREELAND JR, MAY M, LODGE R & CONRAD KF. 2003. Genetic diversity and widespread haplotypes in a migratory dragonfly, the common green darner Anax junius. Ecol Entomology 28(4): 413-421.
• FREGONEZI JN, TURCHETTO C, BONATTO SL & FREITAS LB. 2013. Biogeographical history and diversification of Petunia and Calibrachoa (Solanaceae) in the Neotropical Pampas grassland. Bot J Linn Soc 171(1): 140-153.
• FU YX. 1997. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147(2): 915-925.
• GIOVENARDI R, DI MARE RA, HENDRIK MIELKE OH, CASAGRANDE M & CARNEIRO E. 2013. Butterflies of Rio Grande do Sul, Brazil (Lepidoptera: Papilionoidea, Hesperioidea). Rev Colomb Entomol 39(2): 267-275.
• GRICE H, NUNEZ-BUSTOS E, MEGA N, DIAS F, ROSA A, FREITAS A & MARINI-FILHO O. 2019. Euryades corethrus (amended version of 2018 assessment). The IUCN Red List of Threatened Species 2019: e. T160549A145166527.
• HAJIBABAEI M, SINGER GAC, HEBERT PDN & HICKEY DA. 2007. DNA barcoding: how it complements taxonomy, molecular phylogenetics and population genetics. Trends Genet 23(4): 167-172.
• HASEGAWA M, KISHINO H & YANO T. 1985. Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J Mol Evol 22(2): 160-174.
• HASTINGS A & HARRISON S. 1994. Metapopulation Dynamics and Genetics. Annu Rev Ecol Syst 25(1): 167-188.
• HEINRICHS JA, LAWLER JJ, SCHUMAKER NH, WILSEY CB & BENDER DJ. 2015. Divergence in sink contributions to population persistence: Sink Contributions to Persistence. Conserv Biol 29(6): 1674-1683.
• HEBERT PDN, CYWINSKA A, BALL SL & DEWAARD JR. 2003. Biological identifications through DNA barcodes. Proceedings. Biological Sciences 270(1512): 313-321. https://doi.org/10.1098/rspb.2002.2218.
  » https://doi.org/10.1098/rspb.2002.2218
• HOHENLOHE PA, FUNK WC & RAJORA OP. 2021. Population genomics for wildlife conservation and management. Mol Ecol 30(1): 62-82.
• HOLSINGER KE & WEIR BS. 2009. Genetics in geographically structured populations: defining, estimating and interpreting FST. Nat Rev Genet 10(9): 639-650.
• INSTITUTO AMBIENTAL DO PARANÁ. 2007. Fauna do Paraná em extinção. Instituto Ambiental do Paraná, Curitiba.
• JOHANSSON M, PRIMMER CR & MERILÄ J. 2007. Does habitat fragmentation reduce fitness and adaptability? A case study of the common frog (Rana temporaria). Mol Ecology 16(13): 2693-2700.
• KAWAKITA A & KATO M. 2009. Repeated independent evolution of obligate pollination mutualism in the Phyllantheae-Epicephala association. P Roy Soc B-Biol Sci 276(1656): 417-426.
• KLITZKE CF & BROWN KS. 2000. The occurrence of aristolochic acids in neotropical troidine swallowtails (Lepidoptera: Papilionidae). Chemoecology 102(10): 99-102.
• KUCHTA SR & TAN AM. 2004. Isolation by distance and post-glacial range expansion in the rough-skinned newt, Taricha granulosa: phylogeography of the rough-skinned newt. Mol Ecol 14(1): 225-244. https://doi.org/10.1111/j.1365-294X.2004.02388.x.
  » https://doi.org/10.1111/j.1365-294X.2004.02388.x
• LANDRES PB, VERNER J & THOMAS JW. 1988. Ecological Uses of Vertebrate Indicator Species: A Critique. Conserv Biol 2(4): 316-328. https://doi.org/10.1111/j.1523-1739.1988.tb00195.x.
  » https://doi.org/10.1111/j.1523-1739.1988.tb00195.x
• LAWLER JJ ET AL. 2006. Conservation science: a 20-year report card. Front Ecol Environ 4(9): 473-480.
• LEIGH JW & BRYANT D. 2015. popart: full-feature software for haplotype network construction. Methods Ecol Evol 6(9): 1110-1116.
• LIU J, BAO Y, WANG Y, SUN B & YE B. 2013. Effects of islanding on the genetics of Niviventer confucianus (Mamalia: Rodentia: Muridae) populations in the Thousand Island Lake region. J Nat Hist 47(39-40): 2583-2598. https://doi.org/10.1080/00222933.2013.773098.
  » https://doi.org/10.1080/00222933.2013.773098
• LV K, WANG J-R, LI T-Q, ZHOU J, GU J-Q, ZHOU G-X & XU Z-H. 2019. Effects of habitat fragmentation on the genetic diversity and differentiation of Dendrolimus punctatus (Lepidoptera: Lasiocampidae) in Thousand Island Lake, China, based on mitochondrial COI gene sequences. Bull Entomol Res 109(1): 62-71.
• MAES D, VANREUSEL W, TALLOEN W & DYCK HV. 2004. Functional conservation units for the endangered Alcon Blue butterfly Maculinea alcon in Belgium (Lepidoptera: Lycaenidae). Biol Conserv 120(2): 229-241.
• MAPELLI FJ, MORA MS, MIROL PM & KITTLEIN MJ. 2012. Effects of Quaternary climatic changes on the phylogeography and historical demography of the subterranean rodent Ctenomys porteousi. J Zool 286(1): 48-57.
• MEGA NO, GUIMARÃES M, COSTA MC, CAPORALE A, PAESI RA, FUCILINI LL & ROMANOWSKI HP. 2020. Population biology and natural history of the grassland butterfly Euryades corethrus (Papilionidae: Troidini), an endangered species from South American Campos. J Insect Conserv 24(5): 853-865.
• MEGA NO & REVERS LF. 2011. Developing a rapid, efficient and low cost method for rapid DNA extraction from arthropods. Cienc Rural 41(9): 1563-1570.
• MENDONÇA MDS, WINCK B, BALDISSERA R, DRÖSE W, DIAS CF, DAVID MZ, TOMA TSP & PODGAISKI LR. 2015. Biodiversidade de Artrópodes. In: Os Campos do Sul (1st ed., p. 61-69). Rede Campos Sulinos - UFRGS. https://doi.org/10.13140/rg.2.1.3873.3922
  » https://doi.org/10.13140/rg.2.1.3873.3922
• MORA MS, CUTRERA AP, LESSA EP, VASSALLO AI, D’ANATRO A & MAPELLI FJ. 2013. Phylogeography and population genetic structure of the Talas tuco-tuco (Ctenomys talarum): integrating demographic and habitat histories. J Mammal 94(2): 459-476.
• NORDBORG M & INNAN H. 2002. Molecular population genetics. Curr Opin Plant Biol 5(1): 69-73.
• OATES M. 1992. The role of butterfly releases in Great Britain & Europe. Future of Butterflies in Europe: Strategies for Survival. Agricultural University, Wageningen 204-212.
• OLIVER JC, TONG X-L, GALL LF, PIEL WH & MONTEIRO A. 2012. A Single Origin for Nymphalid Butterfly Eyespots Followed by Widespread Loss of Associated Gene Expression. PLOS Genetics 8(8): e1002893.
• OVERBECK GE, MÜLLER SC, FIDELIS A, PFADENHAUER J, PILLAR VD, BLANCO CC, BOLDRINI II, BOTH R & FORNECK ED. 2007. Brazil’s neglected biome: The South Brazilian Campos. Perspect Plant Ecol Evol Syst 9(2): 101-116. https://doi.org/10.1016/j.ppees.2007.07.005.
  » https://doi.org/10.1016/j.ppees.2007.07.005
• PARR CL, LEHMANN CER, BOND WJ, HOFFMANN WA & ANDERSEN AN. 2014. Tropical grassy biomes: misunderstood, neglected, and under threat. Trends Ecol Evol 29(4): 205-213.
• PEÇANHA WT, ALTHOFF SL, GALIANO D, QUINTELA FM, MAESTRI R, GONÇALVES GL & FREITAS TRO. 2017. Pleistocene climatic oscillations in Neotropical open areas: Refuge isolation in the rodent Oxymycterus nasutus endemic to grasslands. PLoS ONE 12(11): e0187329.
• PFEILER E, JOHNSON S & MARKOW TA. 2012. Insights into Population Origins of Neotropical Junonia (Lepidoptera: Nymphalidae: Nymphalinae) Based on Mitochondrial DNA. Psyche 2012: e423756.
• PETIT-MARTY N, VÁZQUEZ-LUIS M & HENDRIKS IE. 2021. Use of the nucleotide diversity in COI mitochondrial gene as an early diagnostic of conservation status of animal species. Conserv Lett 14(1): e12756. https://doi.org/10.1111/conl.12756.
  » https://doi.org/10.1111/conl.12756
• PILLAR VDP & VÉLEZ E. 2010. Extinção dos Campos Sulinos em Unidades de Conservação: um Fenômeno Natural ou um Problema ético? Nat Conservacao 08(01): 84-86.
• PRENTICE IC & JOLLY D. 2000. Mid-Holocene and glacial-maximum vegetation geography of the northern continents and Africa. J Biogeogr 27(3): 507-519.
• PUTZ FE & REDFORD KH. 2010. The Importance of Defining ‘Forest’: Tropical Forest Degradation, Deforestation, Long-term Phase Shifts, and Further Transitions. Biotropica 42(1): 10-20.
• RAJAEI SH H, RÖDDER D, WEIGAND AM, DAMBACH J, RAUPACH MJ & WÄGELE JW. 2013. Quaternary refugia in southwestern Iran: insights from two sympatric moth species (Insecta, Lepidoptera). Org Divers Evol 13(3): 409-423.
• RAY N, CURRAT M, & EXCOFFIER L. 2003. Intra-Deme Molecular Diversity in Spatially Expanding Populations. Mol Biol Evol 20(1): 76-86. https://doi.org/10.1093/molbev/msg009.
  » https://doi.org/10.1093/molbev/msg009
• ROZAS J, FERRER-MATA A, SÁNCHEZ-DELBARRIO JC, GUIRAO-RICO S, LIBRADO P, RAMOS-ONSINS SE & SÁNCHEZ-GRACIA A. 2017. DnaSP 6: DNA Sequence Polymorphism Analysis of Large Data Sets. Mol Biol Evol 34(12): 3299-3302.
• ROZEWICKI J, LI S, AMADA KM, STANDLEY DM & KATOH K. 2019. MAFFT-DASH: integrated protein sequence and structural alignment. Nucleic Acids Res 47(W1): W5-W10.
• SANTA CATARINA. 2011. RESOLUÇÃO CONSEMA No 002, de 06 de dezembro de 2011 Reconhece a Lista Oficial de Espécies da Fauna Ameaçadas de Extinção no Estado de Santa Catarina e dá outras providências.
• SCHMITT T & SEITZ A. 2001. Allozyme variation in Polyommatus coridon (Lepidoptera: Lycaenidae): identification of ice-age refugia and reconstruction of post-glacial expansion: Biogeography of Polyommatus coridon. J Biogeogr 28(9): 1129-1136.
• SCHMITT T & SEITZ A. 2002. Influence of habitat fragmentation on the genetic structure of Polyommatus coridon (Lepidoptera:Lycaenidae): implications for conservation. Biol Conserv 291-297.
• SCHNEIDER S & EXCOFFIER L. 1999. Estimation of Past Demographic Parameters From the Distribution of Pairwise Differences When the Mutation Rates Vary Among Sites: Application to Human Mitochondrial DNA. Genetics 152(3): 1079-1089.
• SERAPHIM N, BARRETO MA, ALMEIDA GSS, ESPERANÇO AP, MONTEIRO RF, SOUZA AP, FREITAS AVL & SILVA-BRANDÃO KL. 2016. Genetic diversity of Parides ascanius (Lepidoptera: Papilionidae: Troidini): implications for the conservation of Brazil’s most iconic endangered invertebrate species. Conserv Genet 17(3): 533-546.
• SILVA-BRANDÃO KL, SILVA OABNE, BRANDÃO MM, OMOTO C & SPERLING FAH. 2015. Genotyping-by-sequencing approach indicates geographic distance as the main factor affecting genetic structure and gene flow in Brazilian populations of Grapholita molesta (Lepidoptera, Tortricidae). Evol Appl 8(5): 476-485. https://doi.org/10.1111/eva.12257.
  » https://doi.org/10.1111/eva.12257
• SLATKIN M. 1987. Gene flow and the geographic structure of natural populations. Science 236(4803): 787-792.
• SLATKIN M & HUDSON RR. 1991. Pairwise comparisons of mitochondrial DNA sequences in stable and exponentially growing populations. Genetics 129(2): 555-562.
• STICKLER CM, NEPSTAD DC, COE MT, MCGRATH DG, RODRIGUES HO, WALKER WS, SOARES-FILHO BS & DAVIDSON EA. 2009. The potential ecological costs and cobenefits of REDD: a critical review and case study from the Amazon region. Glob Change Biol 15(12): 2803-2824.
• TAJIMA F. 1989. Statistical Method for Testing the Neutral Mutation Hypothesis by DNA Polymorphism. Genetics 123(3): 585-595.
• TILMAN D, MAY RM, LEHMAN CL & NOWAK MA. 1994. Habitat destruction and the extinction debt. Nature 371(6492): 65-66.
• TODISCO V, GRATTON P, ZAKHAROV EV, WHEAT CW, SBORDONI V & SPERLING FAH. 2012. Mitochondrial phylogeography of the Holarctic Parnassius phoebus complex supports a recent refugial model for alpine butterflies. J Biogeogr 39(6): 1058-1072.
• TURCHETTO-ZOLET AC, PINHEIRO F, SALGUEIRO F & PALMA-SILVA C. 2012. Phylogeographical patterns shed light on evolutionary process in South America. Mol Ecol 22(5): 1193-1213. https://doi.org/10.1111/mec.12164.
  » https://doi.org/10.1111/mec.12164
• TYLER HA, BROWN KS & WILSON KH. 1994. Swallowtail butterflies of the Americas: a study in biological dynamics, ecological diversity, biosystematics, and conservation. 1st ed. Gainesville: Scientific Publishers, 376 p.
• VÉLEZ-MARTIN E, ROCHA CH, BLANCO C, AZAMBUJA BO & HASENACK H. 2015. Conversão e Fragmentação. In LANGE O & PILLAR VDP (Eds), Os Campos do Sul, Porto Alegre:Rede Campos Sulinos - UFRGS, p. 123-132.
• VOLKMANN L & NÚÑEZ-BUSTOS E. 2010. Mariposas serranas de Argentina central: Papilionidae, Pieridae Lycaenidae y Riodinidae. Cordoba: Equipo Grafico, 140 p.
• WANG H, FAN X, OWADA M, WANG M & NYLIN S. 2014. Phylogeny, Systematics and Biogeography of the Genus Panolis (Lepidoptera: Noctuidae) Based on Morphological and Molecular Evidence. PLoS ONE 9(3): e90598.
• WANG R, COMPTON SG & CHEN X-Y. 2011. Fragmentation can increase spatial genetic structure without decreasing pollen-mediated gene flow in a wind-pollinated tree. Mol Ecol 20(21): 4421-4432.
• WHITLOCK MC. 2004. Selection and Drift in Metapopulations. In Ecology, Genetics and Evolution of Metapopulations. In: HANSKI I & GAGGIOTTI O (Eds), Ecology, Genetics and Evolution of Metapopulations, Massachusetts: Academic Press, p. 153-173.
• WILSON JB, PEET RK, DENGLER J & PÄRTEL M. 2012. Plant species richness: The world records. J Veg Sci 23(4): 796-802.

Publication Dates

• Publication in this collection
  16 June 2023
• Date of issue
  2023

History

• Received
  1 Apr 2021
• Accepted
  10 June 2021